Gradient Descent for Multiple Variables MCQ

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• Mean normalization can be used to simplify gradient descent for multivariate linear regression.
  ক

  True

  খ

  False
• Who introduced the topic of gradient descent?
  ক

  Vapnik

  খ

  Augustin-Louis Cauchy

  গ

  Chervonenkis

  ঘ

  Alan Turing
• What is updated by gradient descent after each iteration?
  ক

  The learning rate

  খ

  Independent variables

  গ

  Target variable

  ঘ

  The number of training examples
• On which factor is the updating of each parameter dependent on?
  ক

  The number of training examples

  খ

  Target variable

  গ

  The learning rate and the target variable

  ঘ

  The learning rate
• x1’s range is 0 to 300. x2’s range is 0 to 1000. What are the suitable ranges of x1 and x2 after feature scaling?
  ক

  x1 = x1 – 300, x2 = x2 – 1000

  খ

  x1 = x2 – 700

  গ

  x1 = x1/1000, x2 = x2/ 300

  ঘ

  x1 = x1/300, x2 = x2/1000
• x1’s range is 0 to 300. x2’s range is 0 to 1000. What are the suitable ranges of x1 and x2 after mean normalization?
  ক

  x1 = (x1 – 150)/300, x2 = (x2-500)/1000

  খ

  x1 = x2 – 700

  গ

  x1 = x1 – 300, x2 = x2 – 1000

  ঘ

  x1 = x1/300, x2 = x2/1000
• Feature scaling can be used to simplify gradient descent for multivariate linear regression.
  ক

  True

  খ

  False
• Gradient descent tries to _____________
  ক

  maximize the cost function

  খ

  minimize the cost function

  গ

  minimize the learning rate

  ঘ

  maximize the learning rate.
• When was gradient descent invented?
  ক

  1847

  খ

  1947

  গ

  1857

  ঘ

  1957
• What happens when the learning rate is low?
  ক

  It always reaches the minima quickly

  খ

  It reaches the minima very slowly

  গ

  It overshoots the minima

  ঘ

  Nothing happens
• What is the minimum number of parameters of the gradient descent algorithm?
  ক

  1

  খ

  2

  গ

  3

  ঘ

  4
• The cost function is minimized by __________
  ক

  Linear regression

  খ

  Polynomial regression

  গ

  PAC learning

  ঘ

  Gradient descent